On Tue, 2 Dec 2008, Jarle Brinchmann wrote:
Yes I think so if the errors were normally distributed. Unfortunately
I'm far from that but the combination of sem & its bootstrap is a good
way to deal with it in the normal case.
I must admit as a non-statistician I'm a not 100% sure what the
differ
Yes I think so if the errors were normally distributed. Unfortunately
I'm far from that but the combination of sem & its bootstrap is a good
way to deal with it in the normal case.
I must admit as a non-statistician I'm a not 100% sure what the
difference (if there is one) between a linear functio
Isn't this a special case of structural equation modeling, handled
by the 'sem' package?
Spencer
Jarle Brinchmann wrote:
Thanks for the reply!
On Tue, Dec 2, 2008 at 6:34 PM, Prof Brian Ripley <[EMAIL PROTECTED]> wrote:
I wonder if you are using this term in its correct technic
Thanks for the reply!
On Tue, Dec 2, 2008 at 6:34 PM, Prof Brian Ripley <[EMAIL PROTECTED]> wrote:
> I wonder if you are using this term in its correct technical sense.
> A linear functional relationship is
>
> V = a + bU
> X = U + e
> Y = V + f
>
> e and f are random errors (often but not necessa
I wonder if you are using this term in its correct technical sense.
A linear functional relationship is
V = a + bU
X = U + e
Y = V + f
e and f are random errors (often but not necessarily independent) with
distributions possibly depending on U and V respectively.
and pairs from (X,Y) are obse
I wonder if you are using this term in its correct technical sense.
A linear functional relationship is
V = a + bU
X = U + e
Y = V + f
e and f are random errors (often but not necessarily independent) with
distributions possibly depending on U and V respectively.
and pairs from (X,Y) are obse
[apologies if this appears twice]
Hi,
I have a situation where I have a set of pairs of X & Y variables for
each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and
PDF(y_i)'s are unfortunately often rather non-Gaussian although most
of the time not multi-modal.
For these data (est
Hi,
I have a situation where I have a set of pairs of X & Y variables for
each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and
PDF(y_i)'s are unfortunately often rather non-Gaussian although most
of the time not multi--modal.
For these data (estimates of gas content in galaxies)
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